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8/13/14

Symposium Celebrating 50 Years of Traffic Flow Theory

2014 TFT Summer Meeting

August 11-13, Portland, Oregon USA

Data fusion solution to fix the cumulative drift

problem on urban arterials

(2)

Context

In the Netherlands

Virtually 100% vehicle actuated

traffic controllers: inductive

loops measuring flows (and in)

Last five years: huge

investments in urban

monitoring, particularly in AVI

systems (cams, BT)

TRAVEL TIMES

REALISED ROUTES

PARTIAL OD RELATIONS

Usefulness for urban traffic

management debated …

(3)

Overview

1.

Deducing vehicle accumulation using vehicle counts (cum

curves) is straightforward …

2.

Problem: cumulative drift due to errors in counts

3.

Solution: (f)use counts (with) measured travel times

(4)

t

2

t (time)

n (vehicles)

N(t) = n

1

-n

2

n

2

n

1

n

1th

vehicle

TT(t)= t

2

-t

1

t

1

Cross section x

1

•  Flow: q

(veh/u)

Cross section x

2

•  Flow: q

(veh/u)

Q

1

(t)

d

dt

Q

1

(t)

= q

1

(t)

Q

2

(t)

d

dt

Q

2

(t)

= q

2

(t)

(5)

The cumulative drift problem

Source errors (miscounts, double counts):

lane changes, power failure, etc.

Errors may be random or structural (bias)

Consequence:

With e.g.

Occurs when q

1

(t) and q

2

(t) contain errors

N (t)

= q

1

(s) ds

t

− q

2

(s) ds

t

q

i

(t)

= ˆq

i

(t)

+

ε

i

(t)

N (t)

= ˆN(t) +

t

(

ε

1

(s)

ε

2

(s)

)

ds

ε

i

(t) ~ N (

µ,σ )

(6)

The cumulative drift problem

Source errors (miscounts, double counts):

lane changes, power failure, etc.

Errors may be random or structural (bias)

Consequence:

Occurs when q

1

(t) and q

2

(t) contain errors

N (t)

= q

1

(s) ds

t

− q

2

(s) ds

t

q

i

(t)

= ˆq

i

(t)

+

ε

i

(t)

N (t)

= ˆN(t) +

t

(

ε

1

(s)

ε

2

(s)

)

ds

This is a random walk!

(which means vehicle accumulation is

practically unobservable using counts)

(7)
(8)

t

2

t (time)

n (vehicles)

n

2

t

1

Q

1

(t)

Q

2

(t)

raw da

ta

raw data

TT

!

r

(t

2

)

Solution

n

0

t

0

(9)

t

2

t (time)

n (vehicles)

n

2

t

1

Q

1

(t)

Q

2

(t)

raw da

ta

raw data

n

1

TT

!

r

(t

2

)

TT

r obs

(t

2

)

ε

TT

(t

2

)

Solution

Implies we either

•  Underestimated inflow

•  Overestimated outflow

•  Or both

n

0

t

0

(10)

t

2

t (time)

n (vehicles)

n

2

t

1

Q

1

(t)

Q

2

(t)

corre

cted

raw da

ta

raw data

n

1

Correction factor

is proportional to

ε

TT

TT

!

r

(t

2

)

TT

r obs

(t

2

)

ε

TT

(t

2

)

Solution

n

0

t

0

(11)

TT

!

r

(t

2

)

TT

r obs

(t

2

)

ε

TT

(t

2

)

n

2

n (vehicles)

t

2

Q

1

(t)

Q

2

(t)

t

0

=

Q

2

(t

2

)

n

0

t (time)

t

*

= Q

1 −1

(n

2

)

Q

1

*

(t)

Full weight

Inflow

Q

1

(t

1

)

→ Q

1 *

(t

1

)

ε

N

(t

2

)

Q

2

*

(t)

Full weight

outflow

Q

2

(t

2

)

→ Q

2 *

(t

2

)

ε

N

(t

2

)

t

2

− TT

r obs

(t

2

)

(12)

Solution turns out to be

Correction factor can be expressed

as function of known quantities only

Or more generally

A simple parameter-free correction algorithm

ε

N

(t

2

)

ε

TT

(t

2

)

=

n

2

− n

0

t

*

− t

0

ε

N

(t

i

)

=

ε

TT

(t

i

)

Q

i

(t

i

)

− n

0

Q

i

−1

−1

(n

i

)

− Q

i

−1

−1

(n

0

)

(13)

Results

Rows (random errors): {1%, 5%, 10%}

Columns (bias):

{-5%, 0, 5%};

(14)

Results

Rows (random errors): {1%, 5%, 10%}

Columns (bias):

{-5%, 0, 5%};

(15)

Discussion

Good news for urban traffic management agencies:

Algorithm works offline or online (although with a time lag of course)

Quite a few puzzles to solve:

Limits algorithm (magnitude and nature of errors)

What to do when no closed counting situation?

What to do when no measured travel times?

(16)

Next steps …

Solve puzzles

Pubs:

TRB2015 paper:

Basic idea + extension to multiple links

TFT50 / special issue jnl paper

Basics TRB Paper

+ combination with additional methods

Cytaty

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